code: def observe(self, X: list[Suggestion], y: list[float]) -> None:
      for params, objective_value in zip(X, y):
          sid = hash(frozenset(params.items()))
          trial = self.current_trials.pop(sid)
          self.study.tell(trial, objective_value)
  
file_path: optuna/benchmarks/bayesmark/optuna_optimizer.py
imports: from __future__ import annotations
  from typing import Any
  from typing import Dict
  from typing import Union
  import numpy
  from bayesmark.abstract_optimizer import AbstractOptimizer
  from bayesmark.experiment import experiment_main
  import optuna
  from optuna import pruners
  from optuna import samplers
  from optuna.integration.botorch import BoTorchSampler
  from optuna.integration.cma import PyCmaSampler
label_desc: Use image node_id 4 for calling the OptunaOptimizer obj's underlying member method code with example usage: obj.observe(X, y) without return types